Unfortunately, the environments employed in previous meta-RL research have tended to satisfy one of the above desiderata at the expense of the other. Task distributions such as bandit problems have furnished accessibility, allowing for principled analysis and interpretation of meta-learning performance [1]}, [2]}, [3]}, but have failed on the interestingness front by focusing on very simple task parameterization structures. At the other end of the spectrum, tasks with more interesting and diverse structure (e.g., Atari games) have been grouped together as task distributions, but the underlying structure or parameterization of those distributions is not transparent
[4]}, [5]}, [6]}, [7]}, [8]}, [9]}. This makes it difficult to be confident (beyond human intuition) whether any sample from the task distribution in fact supports transfer to further sampled tasks, let alone to construct Bayes-optimal performance baselines for such transfer.
